scTab

De novo cell type prediction model for single-cell RNA-seq data that can be trained across a large-scale collection of curated datasets.

Model checkpoints and traning data

• Training data (compatible with Merlin Dataloader infrastructure): https://pklab.med.harvard.edu/felix/data/merlin_cxg_2023_05_15_sf-log1p.tar.gz (164GB)
• Model checkpoints: https://pklab.med.harvard.edu/felix/data/scTab-checkpoints.tar.gz (8.1GB)
• Minimal subset of the training, validation and test data: https://pklab.med.harvard.edu/felix/data/merlin_cxg_2023_05_15_sf-log1p_minimal.tar.gz (0.5GB)

Project structure

• cellnet: code for models + data loading infrastructure
• docs:
  • data.md: Details about data preparation
  • models.md: Details about used models
  • classification-evaluation-metrics.md: Details about used evaluation metrics
• notebooks:
  • data_augmentation: Notebooks related to data augmentation → calculation of augmentation vectors + evaluation
  • model_evaluation: Notebooks containing all evaluation code from this paper
  • loss_curve_plotting: Notebooks to plot and compare loss curves
  • store_creation: Notebooks used to create and reproduce the datasets used in this paper
  • training: Notebooks to train models
• notebooks-tutorials:
  • data_loading.ipynb: Example notebook about how to use data loading
  • model_inference.ipynb: Example notebook how to use trained models for inference
• scripts: Scripts used to train models

Installation

Installation via Nvidia Enroot / Docker (easy)

A base docker image with most packages preinstalled can be pulled from here: nvcr.io/nvidia/merlin/merlin-pytorch:23.02

Moreover, the Nvidia Enroot (https://github.com/NVIDIA/enroot) container image which was used to run all the experiments in this paper can be found to download here: https://pklab.med.harvard.edu/felix/data/merlin-2302.sqsh

For ease of use, we recommend to use the above supplied Enroot container image as it comes with all relevant software preinstalled.

Installation via pip

Run the following command the project folder to install the cellnet package: pip install -e .

To install GPU dependencies install the dependencies from the requirements-gpu.txt file first. To do so, use --extra-index-url https://pypi.nvidia.com/ argument when installing packages via pip.

Installation time on a local computer should be a couple of minutes.

System requirements

Operating system: Ubuntu 20.04.5 LTS (used OS version)
Python version: 3.8 or 3.10
Packages: See requirements.txt and requirements-gpu.txt

Hardware requirements

Due to high computational demands, a modern GPU (e.g. Nvidia A100 or V100 GPU with at least 16GB of VRAM) is needed to run the training and evaluation scripts in this repository.
On a normal desktop computer without GPU acceleration runtime will probably exceed several days.

Licence

MIT license

Authors

scTab was written by Felix Fischer <felix.fischer@helmholtz-munich.de>

Support for software development, testing, modeling, and benchmarking provided by the Cell Annotation Platform team (Roman Mukhin, Andrey Isaev, Uğur Bayındır)

Citation

If scTab is helpful in your research, please consider citing the following paper

Fischer, Felix, David S. Fischer, Roman Mukhin, Andrey Isaev, Evan Biederstedt, Alexandra-Chloé Villani, and Fabian J. Theis. 2024. “scTab: Scaling Cross-Tissue Single-Cell Annotation Models.” Nature Communications 15 (1). https://doi.org/10.1038/s41467-024-51059-5.